Director/Senior Director, Molecular Discovery

About the role

You are responsible for ensuring that Lila's autonomous science platform reliably and repeatedly converts molecular hypotheses into validated small-molecule drug candidates at increasing speed and quality. This role centers on owning the end-to-end success of assigned discovery programs, from initial hit identification through lead optimization and final candidate nomination. You will act as the accountable leader for discovery timelines, throughput, and quality benchmarks, ensuring that platform output meets rigorous standards for potency, selectivity, and developability. The position requires deep collaboration with AI, computational, robotics, and engineering teams to diagnose bottlenecks and refine how the platform learns from experiments. You will architect each program end to end, defining the required assays, capabilities, and decision criteria that govern compound progression. The role also involves interfacing with external partners for specialized studies and translating platform strengths into compelling value propositions for pharma and biotech collaborators.

Key facts

What you'll do

Define and drive small molecule discovery programs from initial hypothesis generation to candidate nomination, ensuring rigorous scientific and operational execution.

Set and own discovery timelines, quality metrics, and throughput targets while maintaining accountability across multiple active programs.

Operate as the primary liaison between the autonomous science platform and drug development decision points, translating experimental outcomes into clear strategic guidance.

Partner daily with AI/ML, robotics, and software engineering teams to close the loop between predictions and wet-lab results, improving platform accuracy and efficiency.

Architect the full experimental plan for each program, specifying assays, building appropriate capabilities, and managing the necessary scientific staff and workflows.

Establish and enforce quality standards, assay cascades, and go/no-go decision criteria that determine how compounds advance through the pipeline.

Engage with CROs and external partners for specialized studies such as in vivo pharmacology, safety pharmacology, and DMPK that lie outside the automated platform.

Provide drug discovery expertise to Lila's commercial and product teams, demonstrating how platform capabilities support credible partnership and licensing opportunities.

Champion the translation of AI-driven molecular designs into actionable experiments that de-risk candidates and accelerate progression.

Monitor platform performance metrics, identify root causes of underperformance, and implement corrective actions to sustain high-quality output.

Evaluate emerging methods and technologies, integrating those that demonstrably improve discovery speed, reliability, or compound quality.

Maintain clear documentation of scientific rationales, decisions, and experimental metadata to ensure reproducibility and continuous learning.

Communicate complex scientific and operational status to leadership in a concise, actionable manner that supports informed strategic choices.

Ensure alignment between discovery milestones and business objectives, balancing scientific ambition with practical development considerations.

Requirements

Ph.D. in medicinal chemistry, computational chemistry, chemical biology, or a closely related discipline.

12+ years of experience in small molecule drug discovery from the computational, medicinal chemistry, or program leadership side.

At least 5 years in a senior role advancing compounds from hit identification through lead optimization and candidate selection.

Demonstrated track record of delivering clinical candidates with direct involvement in compound progression decisions.

Deep understanding of medicinal chemistry principles, including structure-activity relationships, synthetic tractability, and multiparameter optimization.

Proven ability to run discovery programs with clear metrics, milestones, and accountability structures.

Strong knowledge of ADMET, DMPK, and the data packages required to advance candidates to IND-enabling studies.

Fluency with AI/ML-driven molecular design approaches and the practical judgment to assess when computational output requires experimental validation.

Effective communication skills for translating complex scientific and operational information to leadership and cross-functional stakeholders.

Nice to have

Direct experience with automated, high-throughput, or closed-loop discovery environments in self-driving labs or robotic platforms.

Hands-on background with robotic synthesis, screening, or data integration in a drug discovery setting.

Experience applying computational chemistry methods in a medicinal chemistry workflow, including SAR analysis and molecular optimization.

Track record of building or influencing operational frameworks that manage throughput, efficiency, and scientific quality.

Familiarity with Lila's specific platform and its capabilities, including how data flows from experiments into models.

Practical notes

This is a full-time position.

Travel may be required for scientific meetings, CRO engagements, or collaborative reviews as needed.

Candidates must be eligible to work in the countries where the role is based, with no work authorization sponsorship mentioned in the source.

No specific deadline for application submission is provided in the source.